This book addresses the topics related to artificial intelligence, internet of things, blockchain technology, and machine learning and bring together researchers, developers, practitioners, and users who are interested in cybersecurity and forensics.
This book addresses the topics related to artificial intelligence, internet of things, blockchain technology, and machine learning and bring together researchers, developers, practitioners, and users who are interested in cybersecurity and forensics.
Prof. Keshav Kaushik is Assistant Professor in the Department of Systemics, School of Computer Science at the University of Petroleum and Energy Studies, Dehradun, India. Dr. Shubham Tayal is Assistant Professor at SR University, Warangal. Dr. Akashdeep Bhardwaj is Professor (Cyber Security & Digital Forensics) at the University of Petroleum & Energy Studies (UPES), Dehradun, India. Dr. Manoj Kumar is Assistant Professor (SG) (SoCS) at the University of Petroleum and Energy Studies, Dehradun.
Inhaltsangabe
1. Detection of Cross-Site Scripting and Phishing Website Vulnerabilities Using Machine Learning. 2. A Review: Security and Privacy Defensive Techniques for Cyber Security Using Deep Neural Networks (DNNs). 3. DNA-Based Cryptosystem for Connected Objects and IoT Security. 4. A Role of Digital Evidence: Mobile Forensics Data. 5. Analysis of Kernel Vulnerabilities Using Machine Learning. 6. Cyber Threat Exploitation and Growth during COVID-19 Times. 7. An Overview of the Cybersecurity in Smart Cities in the Modern Digital Age. 8. The Fundamentals and Potential for Cyber Security of Machine Learning in the Modern World. 9. Qualitative and Quantitative Evaluation of Encryption Algorithms. 10. Analysis and Investigation of Advanced Malware Forensics. 11. Network Intrusion Detection System Using Naïve Bayes Classification Technique for Anomaly Detection. 12. Data Security Analysis in Mobile Cloud Computing for Cyber Security. 13. A Comprehensive Review of Investigations of Suspects of Cyber Crimes. 14. Fault Analysis Techniques in Lightweight Ciphers for IoT Devices.
1. Detection of Cross-Site Scripting and Phishing Website Vulnerabilities Using Machine Learning. 2. A Review: Security and Privacy Defensive Techniques for Cyber Security Using Deep Neural Networks (DNNs). 3. DNA-Based Cryptosystem for Connected Objects and IoT Security. 4. A Role of Digital Evidence: Mobile Forensics Data. 5. Analysis of Kernel Vulnerabilities Using Machine Learning. 6. Cyber Threat Exploitation and Growth during COVID-19 Times. 7. An Overview of the Cybersecurity in Smart Cities in the Modern Digital Age. 8. The Fundamentals and Potential for Cyber Security of Machine Learning in the Modern World. 9. Qualitative and Quantitative Evaluation of Encryption Algorithms. 10. Analysis and Investigation of Advanced Malware Forensics. 11. Network Intrusion Detection System Using Naïve Bayes Classification Technique for Anomaly Detection. 12. Data Security Analysis in Mobile Cloud Computing for Cyber Security. 13. A Comprehensive Review of Investigations of Suspects of Cyber Crimes. 14. Fault Analysis Techniques in Lightweight Ciphers for IoT Devices.
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